Mobile apps have embraced user privacy by moving their data processing to the user’s smartphone. Advanced machine learning (ML) models, such as vision models, can now locally analyze user images to extract insights that drive several functionalities. Capitalizing on this new processing model of locally analyzing user images, we analyze two popular social media apps, TikTok and Instagram, to reveal (1) what insights vision models in both apps infer about users from their image and video data and (2) whether these models exhibit performance disparities with respect to demographics. As vision models provide signals for sensitive technologies like age verification and facial recognition, understanding potential biases in these models is crucial for ensuring that users receive equitable and accurate services. We develop a novel method for capturing and evaluating ML tasks in mobile apps, overcoming challenges like code obfuscation, native code execution, and scalability. Our method comprises ML task detection, ML pipeline reconstruction, and ML performance assessment, specifically focusing on demographic disparities. We apply our methodology to TikTok and Instagram, revealing significant insights. For TikTok, we find issues in age and gender prediction accuracy, particularly for minors and Black individuals. In Instagram, our analysis uncovers demographic disparities in the extraction of over 500 visual concepts from images, with evidence of spurious correlations between demographic features and certain concepts.

Zero Involvement Pairing and Authentication (ZIPA) is a promising technique for auto-provisioning large networks of Internet-of-Things (IoT) devices. Presently, these networks use password-based authentication, which is difficult to scale to more than a handful of devices. To deal with this challenge, ZIPA enabled devices autonomously extract identical authentication or encryption keys from ambient environmental signals. However, during the key negotiation process, existing ZIPA systems leak information on a public wireless channel which can allow adversaries to learn the key. We demonstrate a passive attack called SyncBleed, which uses leaked information to reconstruct keys generated by ZIPA systems. To mitigate SyncBleed, we present TREVOR, an improved key generation technique that produces nearly identical bit sequences from environmental signals without leaking information. We demonstrate that TREVOR can generate keys from a variety of environmental signal types under 4 seconds, consistently achieving a 90-95% bit agreement rate across devices within various environmental sources.

In this work we explored the privacy of the mute button for video conferencing applications. We found that the software switch to mute the microphone is just that, a software switch. They can still access the microphone whenever they would like. We found that Cisco Webex was accessing the microphone while muted and sending audio derived telemetry data to their server which was a privacy violation.

Wireless connectivity is becoming common in increasingly diverse personal devices, enabling various interoperation- and Internet-based applications and services. More and more interconnected devices are simultaneously operated by a single user with short-lived connections, making usable device authentication methods imperative to ensure both high security and seamless user experience. Unfortunately, current authentication methods that heavily require human involvement, in addition to form factor and mobility constraints, make this balance hard to achieve, often forcing users to choose between security and convenience. In this work, we present a novel over-the-air device authentication scheme named AEROKEY that achieves both high security and high usability. With virtually no hardware overhead, AEROKEY leverages ubiquitously observable ambient electromagnetic radiation to autonomously generate spatiotemporally unique secret that can be derived only by devices that are closely located to each other. Devices can make use of this unique secret to form the basis of a symmetric key, making the authentication procedure more practical, secure and usable with no active human involvement. We propose and implement essential techniques to overcome challenges in realizing AEROKEY on low-cost microcontroller units, such as poor time synchronization, lack of precision analog front-end, and inconsistent sampling rates. Our real-world experiments demonstrate reliable authentication as well as its robustness against various realistic adversaries with low equal-error rates of 3.4% or less and usable authentication time of as low as 24 s.

Context-based authentication is a method for transparently validating another device’s legitimacy to join a network based on location. Devices can pair with one another by continuously harvesting environmental noise to generate a random key with no user involvement. However, there are gaps in our understanding of the theoretical limitations of environmental noise harvesting, making it difficult for researchers to build efficient algorithms for sampling environmental noise and distilling keys from that noise. This work explores the information-theoretic capacity of context-based authentication mechanisms to generate random bit strings from environmental noise sources with known properties. Using only mild assumptions about the source process’s characteristics, we demonstrate that commonly-used bit extraction algorithms extract only about 10% of the available randomness from a source noise process. We present an efficient algorithm to improve the quality of keys generated by context-based methods and evaluate it on real key extraction hardware. MOONSHINE is a randomness distiller which is more efficient at extracting bits from an environmental entropy source than existing methods. Our techniques nearly double the quality of keys as measured by the NIST test suite, producing keys that can be used in real-world authentication scenarios.

Autonomous vehicles (AVs) use diverse sensors to understand their surroundings as they continually make safety-critical decisions. However, establishing trust with other AVs is a key prerequisite because safety-critical decisions cannot be made based on data shared from untrusted sources. Existing protocols require an infrastructure network connection and a third-party root of trust to establish a secure channel, which are not always available.In this paper, we propose a sensor-fusion approach for mobile trust establishment, which combines GPS and visual data. The combined data forms evidence that one vehicle is nearby another, which is a strong indication that it is not a remote adversary hence trustworthy. Our preliminary experiments show that our sensor-fusion approach achieves above 80% successful pairing of two legitimate vehicles observing the same object with 5 meters of error. Based on these preliminary results, we anticipate that a refined approach can support fuzzy trust establishment, enabling better collaboration between nearby AVs.

We present FLIC, a distributed software data caching framework for fogs that reduces network traffic and latency. FLIC is targeted toward city-scale deployments of cooperative IoT devices in which each node gathers and shares data with surrounding devices. As machine learning and other data processing techniques that require large volumes of training data are ported to low-cost and low-power IoT systems, we expect that data analysis will be moved away from the cloud. Separation from the cloud will reduce reliance on power-hungry centralized cloud-based infrastructure. However, city-scale deployments of cooperative IoT devices often connect to the Internet with cellular service, in which service charges are proportional to network usage. IoT system architects must be clever in order to keep costs down in these scenarios. To reduce the network bandwidth required to operate city-scale deployments of cooperative IoT systems, FLIC implements a distributed cache on the IoT nodes in the fog. FLIC allows the IoT network to share its data without repetitively interacting with a simple cloud storage service, reducing calls out to a backing store. Our results displayed a less than 2% miss rate on reads. Thus, allowing for only 5% of requests needing the backing store. We were also able to achieve more than 50% reduction in bytes transmitted per second.